Enhanced 3DTV Regularization and Its Applications on HSI Denoising and Compressed Sensing

Peng, Jiangjun; Xie, Qi; Zhao, Qian; Wang, Yao; Leung, Yee; Meng, Deyu

doi:10.1109/tip.2020.3007840

Cited by 112 publications

(60 citation statements)

References 42 publications

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“…The denoised results of the proposed methods are compared with those of some kernel-based denoising methods, including SK [36], SDGNLM [38], AKR [40]. Besides, some methods based on local or global similarity are also considered for comparisons, such as SSAHTV [22], 3DNLM [46], LRMR [47], and E3DTV [48]. The denoised results of all methods are analyzed in visual quality and then some numerical indicators are employed to evaluate the denoised images objectively.…”

Section: Experimental Results and Comparisonsmentioning

confidence: 99%

“…6. [36]; (e) AKR [40]; (f) SDGNLM [38]; (g) 3DNLM [46]; (h) LRMR [47]; (i) E3DTV [48]; (j) 3DGK. Moreover, some pixels are chosen and their spectral residual curves are plotted in Fig.…”

Section: A Simulated Data Experimentsmentioning

confidence: 99%

“…For the proposed method, the residual curve is relatively stable when the band number is smaller than 100. [36]; (e) AKR [40]; (f) SDGNLM [38]; (g) 3DNLM [46]; (h) LRMR [47]; (i) E3DTV [48]; (j) 3DGK.…”

Section: A Simulated Data Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Hyperspectral Image Denoising Using 3-D Geometrical Kernel With Local Similarity Prior

Zhang

Sun

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The majority of existing hyperspectral (HS) image denoising methods exploit local similarity in HS images by rearranging them into the matrix or vector forms. As the typical 3D data, the inherent spatial and spectral properties in HS images should be simultaneously explored for denoising. Therefore, a 3D geometrical kernel (3DGK) is developed in this paper to describe the local structure. The proposed method assumes that the pixel can be represented by other pixels within a 3D block efficiently owing to the local similarity with adjacent positions. Then, the HS image is modeled by the 3D kernel regression with L1-norm constraint, in which the local similarity is captured by the proposed 3DGK. To efficiently compute the parameters in 3DGK, geometrical structures, such as scale, shape, and orientation in the 3D block, are estimated from the gradient information approximately. Finally, the noises are effectively removed while preserving the structures in HS images. Moreover, experimental results on simulated and real datasets demonstrate that the performance of 3DGK is better than those of the methods based on local similarity prior.

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Section: Experimental Results and Comparisonsmentioning

confidence: 99%

“…6. [36]; (e) AKR [40]; (f) SDGNLM [38]; (g) 3DNLM [46]; (h) LRMR [47]; (i) E3DTV [48]; (j) 3DGK. Moreover, some pixels are chosen and their spectral residual curves are plotted in Fig.…”

Section: A Simulated Data Experimentsmentioning

confidence: 99%

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Hyperspectral Image Denoising Using 3-D Geometrical Kernel With Local Similarity Prior

Zhang

Sun

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Compressive sensing (CS) [1,2] is a novel paradigm that recovers signals that are sparse in a certain domain, from a small set of compressed measurements. This paradigm has been widely applied in various signal processing applications [3][4][5][6][7][8], ranging from image [9,10], audio [11][12][13], to video [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…where, xk is an approximation to x with k largest nonzero entries, i.e., x-xk; δ2k is the 2k-order RIC of A. Formula (4) illustrates that the recovery error is proportional to the noise level and the signal tail, that is, the coefficients of the compressible signals follow a power-law decay. It is safe to say that ^ is approximately x.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery

Zhao¹,

Zhu²,

Wu³

2020

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Compressive sensing (CS) is a novel paradigm to recover a sparse signal in compressed domain. In some overcomplete dictionaries, most practical signals are sparse rather than orthonormal. Signal space greedy method can derive the optimal or near-optimal projections, making it possible to identify a few most relevant dictionary atoms of an arbitrary signal. More practically, such projections can be processed by standard CS recovery algorithms. This paper proposes a signal space subspace pursuit (SSSP) method to compute spare signal representations with overcomplete dictionaries, whenever the sensing matrix satisfies the restricted isometry property adapted to dictionary (D-RIP). Specifically, theoretical guarantees were provided to recover the signals from their measurements with overwhelming probability, as long as the sensing matrix satisfies the D-RIP. In addition, a thorough analysis was performed to minimize the number of measurements required for such guarantees. Simulation results demonstrate the validity of our hypothetical theory, as well as the superiority of the proposed approach.

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Deep compressed sensing MRI via a gradient‐enhanced fusion model

Dai

Wang

2023

Medical Physics

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Background: Compressed sensing has been employed to accelerate magnetic resonance imaging by sampling fewer measurements. However, conventional iterative optimization-based CS-MRI are time-consuming for iterative calculations and often share poor generalization ability on multicontrast datasets. Most deep-learning-based CS-MRI focus on learning an end-to-end mapping while ignoring some prior knowledge existed in MR images. Purpose: We propose an iterative fusion model to integrate the image and gradient-based priors into reconstruction via convolutional neural network models while maintaining high quality and preserving the detailed information as well. Methods: We propose a gradient-enhanced fusion network (GFN) for fast and accurate MRI reconstruction, in which dense blocks with dilated convolution and dense residual learnings are used to capture abundant features with fewer parameters. Meanwhile, decomposed gradient maps containing obvious structural information are introduced into the network to enhance the reconstructed images. Besides this, gradient-based priors along directions X and Y are exploited to learn adaptive tight frames for reconstructing the desired image contrast and edges by respective gradient fusion networks. After that, both image and gradient priors are fused in the proposed optimization model, in which we employ the l 2 -norm to promote the sparsity of gradient priors. The proposed fusion model can effectively help to capture edge structures in the gradient images and to preserve more detailed information of MR images. Results: Experimental results demonstrate that the proposed method outperforms several CS-MRI methods in terms of peak signal-to-noise (PSNR), the structural similarity index (SSIM), and visualizations on three sampling masks with different rates. Noteworthy, to evaluate the generalization ability, the proposed model conducts cross-center training and testing experiments for all three modalities and shares more stable performance compared than other approaches. In addition, the proposed fusion model is applied to other comparable deep learning methods.The quantitative results show that the reconstruction results of these methods are obviously improved. Conclusions: The gradient-based priors reconstructed from GFNs can effectively enhance the edges and details of under-sampled data. The proposed fusion model integrates image and gradient priors using l 2 -norm can effectively improve the generalization ability on multicontrast datasets reconstruction.

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Enhanced 3DTV Regularization and Its Applications on HSI Denoising and Compressed Sensing

Cited by 112 publications

References 42 publications

Hyperspectral Image Denoising Using 3-D Geometrical Kernel With Local Similarity Prior

Hyperspectral Image Denoising Using 3-D Geometrical Kernel With Local Similarity Prior

Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery

Deep compressed sensing MRI via a gradient‐enhanced fusion model

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